README.md

---
license: apache-2.0
---
# Qwen-Image Full Distillation Accelerated Model

![](./assets/title.jpg)

## Model Introduction

This model is a distilled and accelerated version of [Qwen-Image](https://www.modelscope.cn/models/Qwen/Qwen-Image). The original model requires 40 inference steps and classifier-free guidance (CFG), resulting in a total of 80 forward passes. In contrast, the distilled accelerated model only requires 15 inference steps without CFG, totaling just 15 forward passes—**achieving approximately 5x speedup**. Of course, the number of inference steps can be further reduced based on requirements, though this may lead to some degradation in generation quality.

The training framework is built upon [DiffSynth-Studio](https://github.com/modelscope/DiffSynth-Studio). The training data consists of 16,000 images generated by the original model using randomly sampled prompts from [DiffusionDB](https://www.modelscope.cn/datasets/AI-ModelScope/diffusiondb). The training process was conducted on 8 * MI308X GPUs and took approximately one day.

## Performance Comparison

||Original Model|Original Model|Accelerated Model|
|-|-|-|-|
|Inference Steps|40|15|15|
|CFG Scale|4|1|1|
|Forward Passes|80|15|15|
|Example 1|![](./assets/image_1_full.jpg)|![](./assets/image_1_original.jpg)|![](./assets/image_1_ours.jpg)|
|Example 2|![](./assets/image_2_full.jpg)|![](./assets/image_2_original.jpg)|![](./assets/image_2_ours.jpg)|
|Example 3|![](./assets/image_3_full.jpg)|![](./assets/image_3_original.jpg)|![](./assets/image_3_ours.jpg)|

## Inference Code

```shell
git clone https://github.com/modelscope/DiffSynth-Studio.git  
cd DiffSynth-Studio
pip install -e .
```

```python
from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig
import torch


pipe = QwenImagePipeline.from_pretrained(
    torch_dtype=torch.bfloat16,
    device="cuda",
    model_configs=[
        ModelConfig(model_id="DiffSynth-Studio/Qwen-Image-Distill-Full", origin_file_pattern="diffusion_pytorch_model*.safetensors"),
        ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors"),
        ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"),
    ],
    tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"),
)
prompt = "精致肖像，水下少女，蓝裙飘逸，发丝轻扬，光影透澈，气泡环绕，面容恬静，细节精致，梦幻唯美。"
image = pipe(prompt, seed=0, num_inference_steps=15, cfg_scale=1)
image.save("image.jpg")
```